Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics
The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sour...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/4/1791 |
| _version_ | 1827755623780974592 |
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| author | Alaelddin F. Y. Mohammed Salman Md Sultan Joohyung Lee Sunhwan Lim |
| author_facet | Alaelddin F. Y. Mohammed Salman Md Sultan Joohyung Lee Sunhwan Lim |
| author_sort | Alaelddin F. Y. Mohammed |
| collection | DOAJ |
| description | The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data. |
| first_indexed | 2024-03-11T08:11:37Z |
| format | Article |
| id | doaj.art-27a30aab18d64e01b6036e3dead5bf1b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:11:37Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-27a30aab18d64e01b6036e3dead5bf1b2023-11-16T23:06:11ZengMDPI AGSensors1424-82202023-02-01234179110.3390/s23041791Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data AnalyticsAlaelddin F. Y. Mohammed0Salman Md Sultan1Joohyung Lee2Sunhwan Lim3School of Computing, Gachon University, Seongnam 13120, Republic of KoreaEuropean IT Solutions Institute, Dhaka 1216, BangladeshSchool of Computing, Gachon University, Seongnam 13120, Republic of KoreaAutonomous IoT Research Section, ETRI, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaThe Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data.https://www.mdpi.com/1424-8220/23/4/1791IoTDQNedge intelligencedata cleaning |
| spellingShingle | Alaelddin F. Y. Mohammed Salman Md Sultan Joohyung Lee Sunhwan Lim Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics Sensors IoT DQN edge intelligence data cleaning |
| title | Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics |
| title_full | Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics |
| title_fullStr | Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics |
| title_full_unstemmed | Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics |
| title_short | Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics |
| title_sort | deep reinforcement learning based iot sensor data cleaning framework for enhanced data analytics |
| topic | IoT DQN edge intelligence data cleaning |
| url | https://www.mdpi.com/1424-8220/23/4/1791 |
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